{"id":"W4306880438","doi":"10.1177/14604582221135427","title":"Machine learning based survival prediction in Glioma using large-scale registry data","year":2022,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute; BC Cancer Agency; National Institutes of Health","keywords":"Concordance; Glioma; Random forest; Medicine; Support vector machine; Proportional hazards model; Predictive modelling; Glioblastoma; Machine learning; Artificial intelligence; Scale (ratio); Oncology; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007592704,0.0004016389,0.0005575279,0.002188167,0.0002281159,0.001053646,0.0006375888,0.0003780368,0.0008223476],"category_scores_gemma":[0.02780332,0.000162726,0.0006906527,0.002539488,0.0002113241,0.001053733,0.0007452377,0.0007199312,0.0003527018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007472535,"about_ca_system_score_gemma":0.00130351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048042,"about_ca_topic_score_gemma":0.01248122,"domain_scores_codex":[0.9975169,0.001605884,0.0002304093,0.0002871009,0.0002315941,0.0001280193],"domain_scores_gemma":[0.9832451,0.01147918,0.002359401,0.001473676,0.001182049,0.0002606086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004263984,0.0001865752,0.8230137,0.0002167039,0.0004316882,0.000237227,0.000195553,0.07268693,0.000517242,0.0011372,0.004452524,0.09649827],"study_design_scores_gemma":[0.0000728986,0.0004586169,0.2923048,0.0002102911,0.0003740046,0.0005226911,0.0006346879,0.6902574,0.002478746,0.006026303,0.006589456,0.00007014081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9390543,0.001839145,0.04471455,0.002028026,0.00007332286,0.0001434518,0.01007906,0.0006218865,0.001446376],"genre_scores_gemma":[0.9824975,0.000345157,0.009974922,0.00005426844,0.00003657283,0.00004861201,0.006871963,0.00001689467,0.0001540846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01048042,"threshold_uncertainty_score":0.04015458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04777481628623458,"score_gpt":0.3517263737823417,"score_spread":0.3039515574961071,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}